As electronic and micromechanical devices decrease in size, there is an increased interest in materials that conduct heat to reduce damage to the structures. Thermal issues are also found in photonic and optoelectrical devices and even on a larger scale with smart material applications. At the same time, there are fundamental scientific questions about the influence of the atomic structure on heat conduction that are not understood. A fertile area for this type of study is Carbon Nanotubes (CNTs) as they span a huge range of thermal conduction depending on their structure: from ~0.1 W/mK for bundled multi-walled CNT systems to above 6000 W/mK for individual single walled CNTs .
We propose a study of thermal transport properties of CNT material using a large variety of CNT free-standing membranes: single-walled and multi-walled, aligned and random, low density and high density, heavily bundled and dispersed. Thermal transport is dominated by phonons, and any change to the matrix can modify their mean free path. It is expected that defects play a major role in the overall thermal transport processes and their engineering becomes essential. Defects in multilayer CNTs are expected to have less impact, due to the presence of neighboring shells that can act as additional phonon channels. Furthermore, it might be possible to alter the thermal coupling between CNTs and thereby strongly influence the thermal properties of a CNT membrane.
These membranes can be heated using a range of options (laser, electrical, furnace) and studied using analytical methods like RBS, TEM, SEM, and Raman to determine the changes that occur. A fundamental understanding and analysis approach of the structural dependencies on the thermal transport of two-dimensional (2D) CNT networks can then be extended to graphene or other 2D materials that are also of interest.
 Kumanek, B. & Janas, D. J Mater Sci (2019) 54: 7397. https://doi.org/10.1007/s10853-019-03368-0
Required background: Material Science, Physics, or equivalent.
Type of work: 50% experimental, 40% modeling/simulation, 10% literature
Supervisor: Stefan De Gendt
Daily advisor: Marina Timmermans
The reference code for this position is 2020-062. Mention this reference code on your application form.